Yong Zhang
About Yong Zhang
Yong Zhang is a Group Scientist in Data & Modeling Science at Procter & Gamble, with a background in Chemical & Biochemical Engineering and Environmental Engineering. He has pioneered the use of Bayesian machine learning and graphical models to enhance consumer understanding and product innovation.
Current Position at Procter & Gamble
Yong Zhang currently holds the position of Group Scientist, Data & Modeling Science at Procter & Gamble. He has been serving in this role since 2022. His work is based in Mason, Ohio, United States.
Previous Roles at Procter & Gamble
Yong Zhang has a significant history with Procter & Gamble. Before his current role, he served as a Senior Scientist, Data & Modeling Science from 2018 to 2022 for 4 years in Mason, Ohio. Prior to that, he worked as a Scientist, Data & Modeling Science from 2015 to 2018 for 3 years in West Chester, OH. His tenure at Procter & Gamble has been marked by his contributions to data and modeling science.
Academic Background
Yong Zhang has a strong academic background. He earned his Doctor of Philosophy (Ph.D.) in Chemical & Biochemical Engineering from Rutgers, The State University of New Jersey-New Brunswick, where he studied from 2008 to 2014. Prior to that, he achieved a Master of Science (MS) degree in Environmental Engineering from Kunming University of Science & Technology, studying from 2004 to 2007. He also holds a Bachelor of Science (BS) degree, with a major in environmental engineering and a double major in biomedical engineering, from the same institution, attained from 2000 to 2004.
Research Experience at Rutgers University
Yong Zhang worked as a Research Assistant at Rutgers University from 2009 to 2014 for 5 years in Piscataway, New Jersey. During this time, he was involved in various research projects, contributing to his expertise in chemical and biochemical engineering.
Innovations at Procter & Gamble
Yong Zhang has led significant innovations at Procter & Gamble. He developed a Bayesian machine learning platform to enhance consumer understanding and accelerate product innovation. Additionally, he played a key role in creating an adviser system that provides personalized and explainable product regimens to consumers. Zhang has also pioneered the use of graphical models, including Probabilistic Graphical Models and Knowledge Graphs, to gain insights into consumer behavior and generate product innovation ideas from unstructured data.